Breast Cancer Prediction Based on the CNN Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In modern society, the natural lifespan of an individual increased dramatically benefitting from advanced yet accurate methods of medical treatment. Though many diseases could be treated with a cure, the treatment of cancer has yet to be overcome. Related medical research has proven that the combination of accurate breast cancer diagnoses and treatments at an early stage could prevent the spread of cancer cells as it could increase a person's potential lifespan by a large margin. This research has conducted a comprehensive study on improving the efficiency of autonomous image recognition of breast cancer diagnosis using deep learning models. We use the most advanced CNN baseline models for image recognition, including VGG, ResNet, Efficient, etc. We also select two typical breast cancer datasets and tested the models on them to make our result more convincing. The final enhanced model of ResNet 101 can achieve a recognition rate of 89.98% for the benign and malignant samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it